The following abbreviations are used within the description below:                CCC cognitive control channel        CR cognitive radio        CRN cognitive radio network        DB database        E-UTRAN evolved UTRAN        FCC federal communications commission (US)        GERAN GSM/EDGE radio access network        GSM global system for mobile telecommunications        ISM industrial, scientific and medical (originally reserved for these uses)        QoS quality of service        UTRAN universal terrestrial radio access network        WS white space        
Traditionally radio spectrum is divided between different radio systems in a manner that strictly allocates a specific band to a specific system. This strict allocation will be changing to a more flexible spectrum utilization at least in some frequency bands in the future. Primary users are those to whom the specific frequency band is licensed such as those operating in hierarchical or other such formal networks (for example, cellular networks such as GSM, GERAN, UTRAN and E-UTRAN; broadcast systems such as television systems; and satellite systems such as GPS and IRIDIUM). Cognitive networks exploit unused time-frequency slots in the radio spectrum in an opportunistic manner. Ad-hoc networks such as WLAN, Bluetooth, ANT and Zigbee for example operate in the ISM band and are therefore not licensed, and not cognitive networks.
Cognitive networks exploit spectrum ‘holes’ within the frequency bandwidth opportunistically. Secondary users are those operating in cognitive networks, outside the structured networks. Since essentially almost all spectrum in crowded areas that is usable by mobile terminals is allocated to some formal network or another, the secondary users find and utilize portions of the existing formal networks' spectrum in an opportunistic manner. Since finding these spectrum holes is power intensive to a mobile device (for example, spectrum sensing by cyclostationary feature detection) and the holes change dynamically based largely on primary user activity, there is some research into coordinating the spectrum sensing function which finds these holes among various secondary users, and also for sharing the whole of the spectrum sensing results among them. Secondary users are generally referred to as cognitive radios CRs or whitespace WS devices. Note that an individual mobile device may operate as a primary user on a licensed band and simultaneously as a secondary user in the whitespaces; the two are not mutually exclusive to a generic device. Development of cognitive radio systems is at an early stage, and some cognitive radio systems may have a specific spectrum band allocated (for a cognitive pilot channel and/or a cognitive control radio, which are more generically termed cognitive control channels CCCs), and may even have some central node.
Some frequency bands are active globally (e.g., cellular bands for globally adopted protocols), which eases burdens on wireless equipment manufacturers but may lead to a particular band being highly active in one country but largely unused in another. Regulatory bodies are beginning to consider how this inefficiently used spectrum may be better put to use. For example, in the United States the FCC has opened the former television bands, named White Spaces, for unlicensed devices which can use that spectrum without interfering with licensed users. Other countries are expected to also allow unlicensed secondary users on certain licensed bands. However, the secondary users need to be able to avoid interfering with the primary (licensed) users, when and where such users are active. This means that secondary users need to detect the primary user.
Very early visions of cognitive radio considered that secondary users would discover the primary users, for whom they must avoid interfering, through spectrum sensing. Other options include having information of the primary users in the area (e.g., network operators and bands/frequencies they occupy) stored in a database DB and broadcast on cognitive pilot channels or accessed by other means such as requesting from that database of distributed by access points. For example, the FCC has a database of TV signals and locations where wireless microphones are being used (such as stadiums, churches and entertainment venues). Current systems using the white spaces as unlicensed users are expected to access to the database relevant to their own geo-location, and also to scan for possible interferences. See for example http://www.showmywhitespace.com/, a WS database showing channels based on a specified address.
Whether or not a particular WS device contributes to the DB its own spectrum sensing results, all WS devices would need to be able to access such a database which accumulates spectrum use information (from WS devices, and/or from access nodes controlling the primary users, etc.). In the DB model the WS device would need to maintain in its own memory the latest information of the DB so as to transmit at the proper frequency bands and times according to the most recent DB information.
A problem arises in the DB model in that the amount of servers and communication capacity of the database is dimensioned based on predictions of how many WS devices might potentially be querying the database simultaneously. However, the traffic load incurred from these queries could be very dynamic in time, and could easily exceed the processing capacity of the database (a DB overload issue). Such a DB overload may substantially increase the risk of server/DB failure, or at least lead to substantial delays in the WS devices obtaining the relevant spectrum use data. Too much of a delay and this time sensitive data is no longer valid. At other times it is expected that the traffic load will be far below the processing capacity of the DB (a DB over-provisioning or over-dimensioning issue). This implies a higher than needed capital investment in the DB infrastructure, which represents less than optimum cost-benefit ratio for database providers.
FIG. 1 illustrates conceptually the overload versus over-dimension issues becoming dominant at different times based on varying traffic load. Any load below the DB capacity represents unused infrastructure, while any load above the DB capacity represents delaying the information to the WS devices and potential DB failure. Exemplary embodiments that are detailed below aid in smoothing those variations in overload versus under load, enabling at least DB providers to more efficiently meet the needs of WS devices.